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010   $a1-299-19786-8
010   $a3-642-35060-7
024 7 $a10.1007/978-3-642-35060-3
035   $a(CKB)2670000000328017
035   $a(EBL)1082872
035   $a(OCoLC)826853749
035   $a(SSID)ssj0000878401
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035   $a(PQKBTitleCode)TC0000878401
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035   $a(DE-He213)978-3-642-35060-3
035   $a(MiAaPQ)EBC1082872
035   $a(PPN)168327929
035   $a(EXLCZ)992670000000328017
100   $a20130125d2013      u|             0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt
182   $cc
183   $acr
200 10$aAdvanced Statistical Methods for Astrophysical Probes of Cosmology$b[electronic resource] /$fby Marisa Cristina March
205   $a1st ed. 2013.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2013.
215   $a1 online resource (191 p.)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $aDescription based upon print version of record.
311   $a3-642-44454-7 
311   $a3-642-35059-3 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- Cosmology background -- Dark energy and apparent late time acceleration -- Supernovae Ia -- Statistical techniques -- Bayesian Doubt: Should we doubt the Cosmological Constant? -- Bayesian parameter inference for SNeIa data -- Robustness to Systematic Error for Future Dark Energy Probes -- Summary and Conclusions -- Index.
330   $aThis thesis explores advanced Bayesian statistical methods for extracting key information for cosmological model selection, parameter inference and forecasting from astrophysical observations. Bayesian model selection provides a measure of how good models in a set are relative to each other - but what if the best model is missing and not included in the set? Bayesian Doubt is an approach which addresses this problem and seeks to deliver an absolute rather than a relative measure of how good a model is.   Supernovae type Ia were the first astrophysical observations to indicate the late time acceleration of the Universe - this work presents a detailed Bayesian Hierarchical Model to infer the cosmological parameters (in particular dark energy) from observations of these supernovae type Ia.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aCosmology
606   $aObservations, Astronomical
606   $aAstronomy?Observations
606   $aStatistics 
606   $aStatistical physics
606   $aDynamical systems
606   $aCosmology$3https://scigraph.springernature.com/ontologies/product-market-codes/P22049
606   $aAstronomy, Observations and Techniques$3https://scigraph.springernature.com/ontologies/product-market-codes/P22014
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
606   $aComplex Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P33000
606   $aStatistical Physics and Dynamical Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P19090
615  0$aCosmology.
615  0$aObservations, Astronomical.
615  0$aAstronomy?Observations.
615  0$aStatistics .
615  0$aStatistical physics.
615  0$aDynamical systems.
615 14$aCosmology.
615 24$aAstronomy, Observations and Techniques.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aComplex Systems.
615 24$aStatistical Physics and Dynamical Systems.
676   $a523.101
700   $aMarch$b Marisa Cristina$4aut$4http://id.loc.gov/vocabulary/relators/aut$0858466
906   $aBOOK
912   $a9910437975503321
996   $aAdvanced Statistical Methods for Astrophysical Probes of Cosmology$91916495
997   $aUNINA